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Model learning based on grid cell representations
Huang GW(黄冠文)1; Si BL(斯白露)2; Tang FZ(唐凤珍)2
作者部门机器人学研究室
会议名称2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
会议日期December 5-8, 2017
会议地点Macau, China
会议主办者Beijing Institute of Technology ; City University of Hong Kong ; IEEE Robotics and Automation Society ; Shenzhen Academy of Robotics ; University of Hong Kong ; University of Macau
会议录名称Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics
出版者IEEE
出版地New York
2017
页码1032-1037
收录类别EI
EI收录号20182905561192
产权排序2
ISBN号978-1-5386-3741-8
摘要Mammals are able to form internal representations of their environments. Place cells found in the hippocampus fire stingily only at a couple of locations of the environment. One synapse away from the hippocampus, grid cells in medial entorhinal cortex discharge bountifully at many locations of the environment, expressing periodic triangular grid firing maps in two-dimensional open field maze. In this study, we investigate the functional advantage of grid codes in the hippocampal-entorhinal circuit from the perspective of model learning. We build neural network models to learn the mapping from space to an abstract variable, which could be used in cognitive processes such as decision-making or motor control. The network using grid code as spatial input achieves better learning accuracy with fewer number of cells than the radial basis function network, which assumes place cell inputs. Our result shows that grid representations constitute better spatial representation in the task of model learning, and may help associative cortex better read out the information held in memory circuits.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22120
专题机器人学研究室
通讯作者Huang GW(黄冠文)
作者单位1.Automation and Electrical Engineering Department, Shenyang Ligong University, Shenyang, China
2.State Key Laboratory of Robotics, Shenyang Institute Of Automation, Chinese Academy Of Science, Shenyang, China
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Huang GW,Si BL,Tang FZ. Model learning based on grid cell representations[C]//Beijing Institute of Technology, City University of Hong Kong, IEEE Robotics and Automation Society, Shenzhen Academy of Robotics, University of Hong Kong, University of Macau. New York:IEEE,2017:1032-1037.
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